Comparar métodos

	Potenciación del Gradiente en Conjunto (Ensemble Gradient Boosting)	AdaBoost	Árbol de Decisión
Campo	Aprendizaje automático	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning	Machine learning
Año de origen	2001	1997	1984
Autor original	Friedman, J. H.	Freund, Y. & Schapire, R.E.	Breiman, Friedman, Olshen & Stone
Tipo	Ensemble (sequential boosting of decision trees)	Ensemble (sequential boosting of weak learners)	Recursive partitioning (if-then rules)
Fuente seminal	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Alias	Gradient Boosting Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Relacionados	6	5	5
Resumen	Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.